Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding Claim 1, Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 1 is directed to a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “calculating a probability value of the occurrence of a failure by a probability mass function (PMF)” “performing a correlation test of the meteorological data and the charging capacity in the charging capacity data to obtain at least one feature factor” As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical relationships and calculations. The limitations: “extracting a number of random failures based on time from the charging capacity data” “decomposing a time series of the charging capacity data, and performing a transformation to obtain time domain-based charging capacity time series data after reducing noise” “establishing a prediction model, using the probability value and feature factor as reference features” As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations: “loaded by a device to carry out the steps of…” “using the charging capacity time series data as a predictive target to train the prediction model” As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f). The limitations: “receiving charging capacity data of a charging facility and meteorological data at where the charging facility is located” As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the receiving limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible. Regarding Claim 2, Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 2 is directed to a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “pre-processing the received charging capacity data and meteorological data, and extracting effective data by an exploratory data analysis” As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper. Step 2A Prong Two Analysis: See corresponding analysis of claim 1. Step 2B Analysis: See corresponding analysis of claim 1. Regarding Claim 3, Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 3 is directed to a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “wherein the pre-processing comprises the step of cleaning at least one selected from the group consisting of an abnormal value and a missing value” As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper. Step 2A Prong Two Analysis: See corresponding analysis of claim 2. Step 2B Analysis: See corresponding analysis of claim 2. Regarding Claim 4, Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 4 is directed to a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: See corresponding analysis of claim 1. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)). The limitations: “ wherein the correlation test is at least one selected from the group consisting of Pearson’s correlation test and Spearman’s rank correlation test ” As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e). Therefore, the additional elements do not integrate the abstract ideas into a practical application. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible. Regarding Claim 5, Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 5 is directed to a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: See corresponding analysis of claim 1. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)). The limitations: “wherein the feature factor is a cumulative rainfall based on the charging capacity data” As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e). Therefore, the additional elements do not integrate the abstract ideas into a practical application. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible. Regarding Claim 6, Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 6 is directed to a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “Inverse Fourier Transform of the time series is performed after the noise reduction, to obtain a charging capacity time series data based on time domain” As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical relationships and calculations. The limitations: “wherein a threshold is set for noise reduction after the time series of the charging capacity data are decomposed by Fast Fourier Transform (FFT)” As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper. Step 2A Prong Two Analysis: See corresponding analysis of claim 1. Step 2B Analysis: See corresponding analysis of claim 1. Regarding Claim 7, Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 7 is directed to a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: See corresponding analysis of claim 1. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)). The limitations: “ wherein the prediction model is a multilayer perception model (MLP), a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, or a self-attention based transformer model ” As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e). Therefore, the additional elements do not integrate the abstract ideas into a practical application. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible. Regarding Claim 8, Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 8 is directed to a charging capacity prediction method based on meteorological factors and charging facility failures, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The limitations: “predicting a future capacity of the charging facility” As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) . The limitations: “by the prediction model” As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstract ideas into a practical application. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” . Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible. Regarding Claim 9, Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 9 is directed to a charging capacity prediction system based on meteorological factors and charging facility failures, which is directed to a machine, one of the statutory categories. Step 2A Prong One Analysis: See corresponding analysis of claim 8. Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations: “A charging capacity prediction system based on meteorological factors and charging facility failures, comprising a processor and at least one storage device, and the storage device storing a system framework that comprises the method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures according to claim 8, and the processor being executed to operate the system framework” As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstract ideas into a practical application. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Xydas et al. (A data-driven approach for characterising the charging demand of electric vehicles: A UK case study) (“ Xydas ”) in view of Li et al. (Intelligent Prognostics for Battery Health Monitoring Using the Mean Entropy and Relevance Vector Machine) (“Li”) in further view of You et al. (U.S. Patent Publication No. 2018/0136285) (“You”). Regarding claim 1, Xydas teaches a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures , loaded by a device to carry out the steps of: ( Xydas Section 2 Data description “The charging events dataset consists of 21,918 charging events from 255 different charging stations and 587 unique EVs drivers… An additional dataset was acquired from the UK Met Office, with information regarding the weather in the Midlands, the geo graphical area under study … The contents of the two datasets are listed in Tables 1 and 2. ”; Section 3.2 Data mining model “The Data Mining Model consists of a Clustering Module, Correlation Module and Regression Module. These modules were used to investigate the shape of the typical daily EVs charging demand profile, the predictability with respect to weather and the trend of EVs charging demand respectively.” Xydas provides a method for predicting charging of EVs based on weather and all charging facility connection and disconnection times data, as shown in Tables 1 and 2 , corresponding establishing a charging capacity prediction model based on meteorological factors and charging facility failures, which includes modules corresponding to a loaded device , wherein the combination below will address the failure data .) receiving charging capacity data of a charging facility and meteorological data at where the charging facility is located ( Xydas Section 2 Data description “Two datasets were provided by Cenex , with information regarding the charging events and charging stations respectively. The charging events dataset con sists of 21,918 charging events from 255 different charging stations and 587 unique EVs drivers. The charging event dataset includes information about the connection/disconnection times and the energy of each charging event for the period of 2012–2013 with event-occurrence granularity… An additional dataset was acquired from the UK Met Office, with information regarding the weather in the Midlands, the geographical area under study. This dataset includes the values of various weather information (e.g. air temperature) with daily granularity for the period of 2012–2013. The weather attributes are listed in Table 3.” Xydas provides receiving data regarding charging events and charging stations respectively, including a corresponding weather dataset, corresponding to receiving charging capacity data of a charging facility and meteorological data at where the charging facility is located.) …performing a correlation test of the meteorological data and the charging capacity in the charging capacity data to obtain at least one feature factor ( Xydas 3.2.2 Correlation module “The Pearson’s Correlation Coefficient (r) was used in this module to measure the correlation between the weather attribute values and the daily peak power of EVs charging demand in a geographical area. The maximum absolute correlation coefficient value of all peak power-weather pairs identifies the most influential weather attribute.” Xydas provides measuring correlation between weather attribute values and the daily peak power of EVs charging demand in a geographical area using Pearson’s Correlation test to identify the most influential weather attribute, corresponding to performing a correlation test of the meteorological data and the charging capacity in the charging capacity data to obtain at least one feature factor.). Xydas fails to teach extracting a number of random failures based on time from the charging capacity data, and calculating a probability value of the occurrence of a failure by a probability mass function (PMF); …decomposing a time series of the charging capacity data, and performing a transformation to obtain time domain-based charging capacity time series data after reducing noise; and establishing a prediction model, using the probability value and feature factor as reference features, and using the charging capacity time series data as a predictive target to train the prediction model. However, Li teaches extracting a number of random failures based on time from the charging capacity data (Li Section II “On the other hand, the battery capacity is the amount of current that the battery can supply over time, which decays over time through charge/discharge cycles, and could be a good measure of the battery health. For this reason, we will employ the capacity as the measure of SOH and later for remaining life prediction, i.e., the SOH data are represented by (1) in this paper. The SOH data are usually random variables arranged in temporal order, which could be considered as time series data … By means of collecting and analyzing the past SOH data, a data-driven model is trained to learn the underlying relationship.”; Section IV “The EOL criteria is generally defined by the manufacturer for a specific application. In this case, the Li-ion battery is considered to fail when its capacity fades by 20% of the initial value, i.e., the cycle life is the number of charge-discharge cycles to 80% of the initial capacity.” Li provides analyzing past battery capacity and charging data to determine a state of health including random variables in the entropy calculation described in Equation (9), wherein the SOH data includes End of Life criteria of the battery indicating failure, corresponding to extracting a number of random failures based on time from the charging capacity data.), and calculating a probability value of the occurrence of a failure by a probability mass function (PMF) (Li Abstract “In this paper, a multistep-ahead prediction model based on the mean entropy and relevance vector machine (RVM) is developed, and applied to state of health (SOH) and remaining life prediction of the battery.”; Section III.B “The entropy of the data set Ti can be computed as Equation (9), where L represents the number of possible outcomes of the random variable, and pl is the histogram or probability mass function.”; Section IV “The predicted failure cycle number based on RVM is 1755 cycles, which is just equal to the actual cycle life of the battery. Fig. 7 also presents the prediction results of RVM, SVM and ARIMA for battery number 2 at cycle number 401, which indicates that the predicted values based on RVM are in close proximity to the actual values.”; Section V “This paper presents a novel data-driven approach for SOH and remaining life predictions based on the mean entropy and RVM.” Li provides calculating a mean entropy to determine a state of health of a battery including a remaining life prediction of a battery using a probability mass function, and predicting the failure cycle of the battery, corresponding to calculating a probability value of the occurrence of a failure by a probability mass function.) …and establishing a prediction model, using the probability value and feature factor as reference features (Li Section II “In order to improve the prediction performance, it is essential to preprocess the measured SOH data. Assume that the denoised SOH data set is denoted as { xk }N k=1, the data set for developing the prediction model can be extracted from { xk }N k=1 in the following form: Equation (3), where N1 = N− mand mis termed the embedding dimension, which represents the number of past SOH data related to the future value. It should be noted that the size of each Ti is m+1. The general time series prediction model can be expressed as Equation (4), where xi =(xi,xi+1,...,xi+m−1) is a vector of the lagged input variables, xi+m is a scalar representing the denoised SOH data at time i + m. In univariate time series prediction.”; Section IV.B “The optimal embedding dimension is chosen by means of estimating the mean entropy values as described in Section III-B… The time series prediction model can then be rewritten as Equation (27)” Li provides establishing a prediction model in accordance with the mean entropy values calculated by the probability mass function of Equation (9), and past battery state of health data in accordance with Equations (3) and (4), respectively corresponding to establishing a prediction model, using the probability value and feature factor as reference features.), and using the charging capacity time series data as a predictive target to train the prediction model (Li Abstract “In this paper, a multistep-ahead prediction model based on the mean entropy and relevance vector machine (RVM) is developed, and applied to state of health (SOH) and remaining life prediction of the battery… Finally, RVM is employed as a novel nonlinear time-series prediction model to predict the future SOH and the remaining life of the battery.”; Section IV.B “The time series prediction model can then be rewritten as Equation (27). is 5. After determining the optimal embedding dimension, the correct time series reconstruction can be realized. Accordingly, time series prediction is performed on the basis of the RVM model by strategy of iterated multistep-ahead prediction. The RVM model is firstly trained by using the past data xi and corresponding data ti , as shown in Table I.” Li provides training a prediction model using charging time series data of a battery, corresponding to using the charging capacity time series data as a predictive target to train the prediction model.). Xydas and Li are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to battery charging predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas with the above teachings of Li. Doing so would provide an accurate remaining battery life estimate to reduce running risks and prevent battery failure (Li Section 1 Introduction “With the aid of health monitoring, an accurate remaining life estimation can reduce the running risks and prevent battery failure.”) Further, You teaches decomposing a time series of the charging capacity data (You [0051] “The training apparatus converts the extracted data to frequency domain data. For example, the training apparatus may convert the extracted data to the frequency domain data by applying a Fourier transform (FT) or Fast Fourier Transform (FFT) to the extracted data.”; [0086] “It is assumed that the training apparatus has acquired sensing data of FIG. 3. The training apparatus classifies the sensing data into sensing data during discharging, that is, discharging data, and sensing data during charging, that is, charging data. As described above, the training apparatus extracts partial data from the discharging data based on a first time interval, and performs FT, for example, fast Fourier transform (FFT)” You provides performing a fast Fourier transform on time domain charging data, wherein the Fast Fourier Transformation corresponds to the decomposition of the time series charging data, corresponding to decomposing a time series of the charging capacity data.), and performing a transformation to obtain time domain-based charging capacity time series data after reducing noise (You [0051] “For example, the training apparatus converts the filtered frequency domain data to the time domain data by applying an inverse Fourier transform (IFT) to the filtered frequency domain data.”; [0070] “For example, the battery state estimation apparatus removes a top 80% of high frequency components through filtering. The battery state estimation apparatus, according to one or more embodiments, converts the filtered frequency domain data to time domain data. For example, the battery state estimation apparatus converts the filtered frequency domain data to the time domain data by applying an inverse Fourier transform (IFT) to the filtered frequency domain data.” You provide an applying an inverse Fourier transform to convert the filtered frequency domain data to the time domain data after filtering a top 80% of high frequency components, wherein the filtering corresponds to the noise reduction, corresponding to performing a transformation to obtain time domain-based charging capacity time series data after reducing noise.); Xydas , Li, and You are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to charging prediction. Therefore it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li with the above teachings of You. Doing so would allow for accurately estimating the lifespan of a battery to determine replacement needs (You [0080] “To determine a timing at which the battery is to be replaced, it is important to accurately estimate the lifespan of the battery.”; [0081] “Accordingly, the state of the battery is significantly more accurately estimated.”). Regarding claim 2, Xydas in view of Li in further view of You teaches further comprising the steps of: pre-processing the received charging capacity data and meteorological data ( Xydas Section 3.1 Data pre-processing model “Data of the Connection Time, Disconnection Time, Energy Drawn, Charging Station ID, Charger Type and County were selected and merged into one dataset ( EVdataset ). The EV dataset and the weather data set were cleaned, removing missing and incorrect values. In the EV dataset , charging events with zero/negative energy were removed from the dataset. Charging events with average charging power higher than the nominal charger rate were corrected by calculating the actual charging duration using the nominal charger power rate… The data pre-processing procedure is presented in Fig. 2” Xydas provides pre-processing the charging and weather dataset, corresponding to pre-processing the received charging capacity data and meteorological data.), and extracting effective data by an exploratory data analysis ( Xydas Section 1 Introduction “Monitoring the charging events will inevitably create large volumes of data. These data require effective data mining methods for their analysis in order to extract useful information.”; Section 3.1 Data pre-processing model “Charging events with average charging power higher than the nominal charger rate were corrected by calculating the actual charging duration using the nominal charger power rate. This consideration is based on the assumption that some EVs may be connected (parked) in a charging station but they are not charging. Therefore, the duration of EVs being connected to a charging station can be different to their actual charging duration. Duplicate data entries were also discovered and removed from both datasets.” Xydas provides effective data mining methods including data analysis and correction during pre-processing based on certain charging assumptions, corresponding to extracting effective data by an exploratory data analysis.). It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You for the same reasons disclosed above in the rejection of claim 1. Regarding claim 3, Xydas in view of Li in further view of You teaches wherein the pre-processing comprises the step of cleaning at least one selected from the group consisting of an abnormal value and a missing value ( Xydas Section 3.1 Data pre-processing model “The EV dataset and the weather dataset were cleaned, removing missing and incorrect values.” Xydas provides data pre-processing including removing missing and incorrect values, corresponding to the pre-processing comprises the step of cleaning at least one selected from the group consisting of an abnormal value and a missing value.). It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You for the same reasons disclosed above in the rejection of claim 2. Regarding claim 4, Xydas in view of Li in further view of You teaches wherein the correlation test is at least one selected from the group consisting of Pearson’s correlation test and Spearman’s rank correlation test ( Xydas 3.2.2 Correlation module “The Pearson’s Correlation Coefficient (r) was used in this module to measure the correlation between the weather attribute values and the daily peak power of EVs charging demand in a geographical area. The maximum absolute correlation coefficient value of all peak power-weather pairs identifies the most influential weather attribute.” Xydas provides measuring correlation between weather attribute values and the daily peak power of EVs charging demand in a geographical area using Pearson’s Correlation test to identify the most influential weather attribute, corresponding to the correlation test is at least one selected from the group consisting of Pearson’s correlation test and Spearman’s rank correlation test.). It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You for the same reasons disclosed above in the rejection of claim 1. Regarding claim 5, Xydas in view of Li in further view of You teaches wherein the feature factor is a cumulative rainfall based on the charging capacity data ( Xydas Table 6 “Weather attribute: Rainfall”; Section 4.2 Influence of weather factors “Table 6 shows the absolute correlation coefficient (r) values between the weather attributes and the daily peak power of EVs charging demand. The most influential factor for all areas was temperature, with the Mean Air Temperature having the highest absolute correlation indices. Leicestershire’s EVs charging demand shows a medium linear correlation, whereas in Nottinghamshire and West Midlands the EVs charging demand has a weaker relationship with weather.” Xydas provides correlation results, as shown in Table 6, which correspond to the feature factors, which further include a monthly cumulative rainfall amount measured on corresponding charging demand data, corresponding to wherein the feature factor is a cumulative rainfall based on the charging capacity data.). It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You for the same reasons disclosed above in the rejection of claim 1. Regarding claim 6, Xydas in view of Li in further view of You teaches wherein a threshold is set for noise reduction after the time series of the charging capacity data are decomposed by Fast Fourier Transform (FFT) (You [0051] “As described above, discharging data may be identified as having a high frequency component and data extracted from the discharging data may also have a high frequency component… For example, the training apparatus may convert the extracted data to the frequency domain data by applying a Fourier transform (FT) or Fast Fourier Transform (FFT) to the extracted data. The training apparatus filters the frequency domain data. The training apparatus inputs the frequency domain data to a high frequency filter to remove the high frequency component, in one or more embodiments. For example, the training apparatus removes approximately a top 80% of high frequency components through filtering.” You provides performing Fast Fourier Transform (FFT) on charging data and subsequently removing a top 80% of high frequency components through filtering, wherein the filtering corresponds to the noise reduction, corresponding to a threshold set for noise reduction after the time series of the charging capacity data are decomposed by Fast Fourier Transform (FFT).), and Inverse Fourier Transform of the time series is performed after the noise reduction, to obtain a charging capacity time series data based on time domain (You [0051] “For example, the training apparatus converts the filtered frequency domain data to the time domain data by applying an inverse Fourier transform (IFT) to the filtered frequency domain data.” You provides performing an inverse Fourier transform to obtain time domain charging data after filtering, corresponding to Inverse Fourier Transform of the time series is performed after the noise reduction, to obtain a charging capacity time series data based on time domain.). Xydas , Li, and You are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to charging prediction. Therefore it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You with the above teachings of You. Doing so would allow for accurately estimating the lifespan of a battery to determine replacement needs (You [0080] “To determine a timing at which the battery is to be replaced, it is important to accurately estimate the lifespan of the battery.”; [0081] “Accordingly, the state of the battery is significantly more accurately estimated.”). Regarding claim 7, Xydas in view of Li in further view of You teaches wherein the prediction model is a multilayer perception model (MLP), a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, or a self-attention based transformer model (You [0058] “Each of the state estimation models corresponding to the charging event and the state estimation model corresponding to the discharging event may include a black-box function where the state estimation model attempts to learn and characterize how the black-box function operates based on inputs provided and outputs generated based on those inputs… As the state estimation model corresponding to each of the charging event and the discharging event, a neural network model, a recurrent neural network (RNN) model, a long short term memory (LSTM) RNN model, a support vector machine (SVM) model, a Gaussian process regression (GPR) model, and the like, may be used.” You provides a recurrent neural network (RNN) model and a long short term memory (LSTM) RNN model.). Xydas , Li, and You are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to charging prediction. Therefore it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You with the above teachings of You. Doing so would allow for accurately estimating the lifespan of a battery to determine replacement needs (You [0080] “To determine a timing at which the battery is to be replaced, it is important to accurately estimate the lifespan of the battery.”; [0081] “Accordingly, the state of the battery is significantly more accurately estimated.”). Regarding claim 8, Xydas in view of Li in further view of You teaches a charging capacity prediction method based on meteorological factors and charging facility failures, comprising the steps of the method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures according to claim 1, as discussed above in the rejection of claim 1 . Further, Li teaches further comprising the steps of predicting a future capacity of the charging facility by the prediction model (Li Abstract “Finally, RVM is employed as a novel nonlinear time-series prediction model to predict the future SOH and the remaining life of the battery.”; Section II Problem Formulation “One uses the battery capacity to represent the SOH. That can be determined by Equation (1), where SOH(k) is the SOH value at cycle number k, Ck is the kth capacity which decreases with age and C0 is the initial capacity.” Section IV “Once the capacity is obtained, the SOH is computed from (1), which is ratio between the current capacity and the initial capacity in the interval [0, 100], where 0 represents very poor health and 100 represents good health. Fig. 2 shows the measured SOH data for battery number 1 and 2, which can be deemed as time series data.” ; Section IV “The data used to validate the proposed approach were collected from two rechargeable Li-ion batteries (referred to as battery numbers 1 and 2),” Li provides predicting future state of health and remaining life of batter ies 1 and 2, which corresponds to the charging facility, including capacity calculations by time-series prediction model, corresponding to predicting a future capacity of the charging facility by the prediction model.). Xydas , Li and You are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to battery charging predictions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You with the above teachings of Li. Doing so would provide an accurate remaining battery life estimate to reduce running risks and prevent battery failure (Li Section 1 Introduction “With the aid of health monitoring, an accurate remaining life estimation can reduce the running risks and prevent battery failure.”). Regarding claim 9, Xydas in view of Li in further view of You teaches a charging capacity prediction system based on meteorological factors and charging facility failures (See e.g. Xydas Section 2 Data description; Li Section II, Section IV ) , comprising a processor and at least one storage device ( See e.g. You [0135] “In one example, a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer.” ), and the storage device storing a system framework that comprises the method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures according to claim 8 ( See e.g. Xydas Section 2 Data description; Li Section II, Section IV ) , and the processor being executed to operate the system framework (You [0076] “For example, the parameter is stored in a memory, and the battery state estimation apparatus acquires the parameter by referring to the memory.”; [0135] “In one example, a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer.” You provides a processor and memory for a battery charging prediction model, corresponding to a processor and at least one storage device, and the storage device storing a system framework and the processor being executed to operate the system framework.). Xydas , Li, and You are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to charging prediction. Therefore it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xydas in view of Li in further view of You with the above teachings of You. Doing so would allow for accurately estimating the lifespan of a battery to determine replacement needs (You [0080] “To determine a timing at which the battery is to be replaced, it is important to accurately estimate the lifespan of the battery.”; [0081] “Accordingly, the state of the battery is significantly more accurately estimated.”). 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